Systems and methods for directional loudspeaker control with facial detection

ABSTRACT

Systems and methods are proposed for adjusting the coverage pattern of a directional loudspeaker array. The methods may comprise adjusting a coverage pattern of a loudspeaker array based on a distribution of audience members. The audience distribution may be determined by imaging a venue with a camera, performing facial detection on the resultant image, and processing the facial detection results to obtain the distribution.

FIELD

The disclosure relates to adjustment of a coverage pattern for aloudspeaker array.

BACKGROUND

Steerable loudspeaker arrays, such as line arrays, provide aconventional method for adjusting the coverage pattern provided by asound system. The coverage patterns may be adjusting using beam steeringtechniques to provide a coverage pattern which is adapted to theparticular architecture of a venue, thereby resulting in a greaterproportion of direct sound received by audience members and reducingunwanted reverberation effects or destructive interference. Steerablesound systems may improve audio quality and listening experience foraudience members in a given venue.

However, steerable loudspeaker arrays suffer from the drawback thatprogramming the array with the desired coverage pattern must typicallybe performed in advance by generating a three-dimensional model of thevenue and obtaining DPS parameters by running simulations with themodel, for example. This may be time-consuming and costly, as well asrequiring specialized knowledge and equipment. In addition, the coveragepattern generated by this method is static, and therefore unable toadapt to changes in audience size, shape, or distribution. This mayresult in significant wastage of power when high-intensity sound isdirected toward regions of a venue with no audience members, forexample. This may also result in sub-optimal sound quality for thoseaudience members who are in the audience, since the loudspeaker arraymay not be optimized for their particular position or distribution.There is thus a need for an adaptive loudspeaker steering system whichcan alter the coverage pattern of the loudspeaker array according to alocation, density, distribution, shape, or number of audience members.

SUMMARY

The above objects may be achieved by, for example, a method, comprisingadjusting a coverage pattern of a loudspeaker array based on adistribution of audience members. This method may utilize a digitalaudio steering system (DASS) and enable the DASS to direct sound energywhere it is needed—for example, regions of a venue with audiencemembers—and to direct reduced sound energy where it is not needed—forexample, regions of a venue with fewer audience members. This adjustmentmay be performed before, during, and/or after reproduction of sound withthe loudspeaker array, or performed periodically or continually inreal-time during sound reproduction, allowing for adaptive automaticcontrol of the coverage pattern, even if the audience distributionchanges throughout a performance, for example. Adjusting a coveragepattern which reduces sending sound energy to regions with feweraudience members may allow for a reduction in the energy consumption ofthe system, as well as reducing unwanted interference effects which mayresult in reverberation affecting sound quality. Therefore, the soundquality and listener experience is improved.

In another example, the present disclosure may provide for a system,comprising a loudspeaker array including a plurality of loudspeakersdirected toward a venue, a camera directed toward the venue, a processorincluding computer-readable instructions stored in non-transitory memoryfor: obtaining an image of the venue with the camera, processing theimage to obtain facial detection data, and selecting a coverage patternfor the loudspeaker array based on the facial detection data.

In still other examples, the above objects may be achieved by a method,comprising receiving an image of a venue from a camera, performingfacial detection on the image to obtain facial detection data, analyzingthe facial detection data to obtain an audience member distribution,adjusting a coverage pattern of a loudspeaker array based on theaudience member distribution to direct a first, greater sound level to afirst region, and a second, lower sound level to a second region, andreproducing sound via the loudspeaker array.

Other systems, methods, features and advantages will be or will becomeapparent to one with skill in the art upon examination of the followingdetailed description and figures. It is intended that all suchadditional systems, methods, features and advantages be included withinthis description, be within the scope of the invention and be protectedby the following claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure may be better understood from reading the followingdescription of non-limiting embodiments, with reference to the attacheddrawings, wherein below:

FIG. 1 shows an example electronic device which may embody aspects ofthe DASS;

FIG. 2 shows an example embodiment of the DASS as a line array andcoverage pattern thereof;

FIG. 3 shows a method for selecting a coverage pattern based on facialdetection;

FIGS. 4A-D show an example of facial detection and selecting a coveragepattern based thereon with a single cluster;

FIG. 5A-D show an example of facial detection and selecting a coveragepattern based thereon with multiple clusters;

FIG. 6 shows a method for identifying clusters of audience members andselecting a coverage pattern based thereon;

FIG. 7A-D show an example of facial detection and selecting a coveragepattern based on audience density obtained therefrom; and

FIG. 8 shows a method for identifying audience density and selecting acoverage pattern based thereon.

DETAILED DESCRIPTION

As noted above, systems and methods for the automatic adjustment of saidcoverage pattern based on a distribution of audience members in a venuedetected by facial detection are provided.

FIG. 1 is a block diagram of an example electronic device 100 that mayinclude one or more aspects of an example DASS. The electronic device100 may include a set of instructions that can be executed to cause theelectronic device 100 to perform one or more of the methods or computerbased functions disclosed, such as imaging a venue, performing facialdetection on the image, determining a coverage pattern based on thefacial detection, adjusting the phase and amplitude of speakers in aspeaker array to achieve the desired coverage pattern, and reproducingsound via the loudspeaker array.

The electronic device 100 may operate as a standalone device or may beconnected, such as using a network, to other computer systems orperipheral devices.

In the example of a networked deployment, the electronic device 100 mayoperate in the capacity of a server or as a client user computer in aserver-client user network environment, as a peer computer system in apeer-to-peer (or distributed) network environment, or in various otherways. The electronic device 100 can also be implemented as, orincorporated into, various electronic devices, such as desktop andlaptop computers, hand-held devices such as smartphones and tabletcomputers, portable media devices such as recording, playing, and gamingdevices, household appliances, office equipment, set-top boxes,automotive electronics such as head units and navigation systems, orother machine capable of executing a set of instructions (sequential orotherwise) that result in actions to be taken by that machine. Theelectronic device 100 may be implemented using electronic devices thatprovide voice, audio, video and/or data communication. While a singleelectronic device 100 is illustrated, the term “device” may include acollection of devices or sub-devices that individually or jointlyexecute a set, or multiple sets, of instructions to perform one or moreelectronic functions of the DASS, elaborated below in greater detail.

The electronic device 100 may include a processor 102, such as a centralprocessing unit (CPU), a graphics processing unit (GPU), or both. Theprocessor 102 may be a component in a variety of systems. For example,the processor 102 may be part of a head unit in a vehicle. Also, theprocessor 102 may include one or more general processors, digital signalprocessors, application specific integrated circuits, field programmablegate arrays, servers, networks, digital circuits, analog circuits,combinations thereof, or other now known or later developed devices foranalyzing and processing data. The processor 102 may implement asoftware program, such as code generated manually or programmed.

The electronic device 100 may include memory, such as a memory 104 thatcan communicate via a bus 110. The memory 104 may be or include a mainmemory, a static memory, or a dynamic memory. The memory 104 may includea non-transitory memory device. The memory 104 may also include computerreadable storage media such as various types of volatile andnon-volatile storage media including random access memory, read-onlymemory, programmable read-only memory, electrically programmableread-only memory, electrically erasable read-only memory, flash memory,a magnetic tape or disk, optical media and the like. Also, the memorymay include a non-transitory tangible medium upon which software isstored. The software may be electronically stored as an image or inanother format (such as through an optical scan), then compiled, orinterpreted or otherwise processed.

In one example, the memory 104 includes a cache or random access memoryfor the processor 102. In alternative examples, the memory 104 may beseparate from the processor 102, such as a cache memory of a processor,the system memory, or other memory. The memory 104 may be or include anexternal storage device or database for storing data. Examples include ahard drive, compact disc (“CD”), digital video disc (“DVD”), memorycard, memory stick, floppy disc, universal serial bus (“USB”) memorydevice, or other device operative to store data. For example, theelectronic device 100 may also include a disk or optical drive unit 108.The drive unit 108 may include a computer-readable medium 122 in whichone or more sets of software or instructions, such as the instructions124, can be embedded. Not depicted in FIG. 1, the processor 102 and thememory 104 may also include a computer-readable medium with instructionsor software.

The memory 104 is operable to store instructions executable by theprocessor 102. The functions, acts or tasks illustrated in the figuresor described may be performed by the programmed processor 102 executingthe instructions stored in the memory 104. The functions, acts or tasksmay be independent of the particular type of instructions set, storagemedia, processor or processing strategy and may be performed bysoftware, hardware, integrated circuits, firmware, microcode and thelike, operating alone or in combination. Likewise, processing strategiesmay include multiprocessing, multitasking, parallel processing and thelike.

The instructions 124 may embody one or more of the methods or logicdescribed herein, including aspects of the electronic device 100 and/oran example digital audio steering system (such as DASS 125). Theinstructions 124 may reside completely, or partially, within the memory104 or within the processor 102 during execution by the electronicdevice 100. For example, software aspects of DASS 125 may includeexamples of the audio signal processor, which may reside completely, orpartially, within the memory 104 or within the processor 102 duringexecution by the electronic device 100.

With respect to the audio signal processor, hardware or softwareimplementations may include analog and/or digital signal processingcomponents and/or instructions (and analog-to-digital and/ordigital-to-analog converters). The analog signal processing componentsand/or instructions may include linear electronic circuits such aspassive filters, active filters, additive mixers, integrators and delaylines. Analog processing components and/or instructions may also includenon-linear circuits such as compandors, multiplicators (frequency mixersand voltage-controlled amplifiers), voltage-controlled filters,voltage-controlled oscillators and phase-locked loops. The digital ordiscrete signal processing components and/or instructions may includesample and hold circuits, analog time-division multiplexers, analogdelay lines and analog feedback shift registers, for example. In otherimplementations, the digital signal processing components and/orinstructions may include ASICs, field-programmable gate arrays orspecialized digital signal processors (DSP chips). Either way, suchdigital signal processing may enhance an audio signal via arithmeticaloperations that include fixed-point and floating-point, real-valued andcomplex-valued, multiplication, and/or addition. Other operations may besupported by circular buffers and/or look-up tables. Such operations mayinclude Fast Fourier transform (FFT), finite impulse response (FIR)filter, Infinite impulse response (IIR) filter, and/or adaptive filterssuch as the Wiener and Kalman filters.

Further, the electronic device 100 may include a computer-readablemedium that includes the instructions 124 or receives and executes theinstructions 124 responsive to a propagated signal so that a deviceconnected to a network 126 can communicate voice, video, audio, imagesor other data over the network 126. The instructions 124 may betransmitted or received over the network 126 via a communication port orinterface 120, or using a bus 110. The communication port or interface120 may be a part of the processor 102 or may be a separate component.The communication port or interface 120 may be created in software ormay be a physical connection in hardware. The communication port orinterface 120 may be configured to connect with the network 126,external media, one or more speakers 112, one or more cameras 113, oneor more sensors 116, or other components in the electronic device 100,or combinations thereof. The connection with the network 126 may be aphysical connection, such as a wired Ethernet connection or may beestablished wirelessly. The additional connections with other componentsof the electronic device 100 may be physical connections or may beestablished wirelessly. The network 126 may alternatively be directlyconnected to the bus 110.

The network 126 may include wired networks, wireless networks, EthernetAVB networks, a CAN bus, a MOST bus, or combinations thereof. Thewireless network may be or include a cellular telephone network, an802.11, 802.16, 802.20, 802.1Q or WiMax network. The wireless networkmay also include a wireless LAN, implemented via WI-FI or BLUETOOTHtechnologies. Further, the network 126 may be or include a publicnetwork, such as the Internet, a private network, such as an intranet,or combinations thereof, and may utilize a variety of networkingprotocols now available or later developed including TCP/IP basednetworking protocols. One or more components of the electronic device100 may communicate with each other by or through the network 126.

The electronic device 100 may also include one or more speakers 112,such as loudspeakers installed in a vehicle, living space, or venue. Theone or more speakers may be part of a stereo system or a surround soundsystem that include one or more audio channels. In particular, speaker112 may comprise an array of speakers. For example, speakers 112 mayinclude a plurality of speakers arranged linearly into a line array.Speakers 112 may comprise multiple line arrays, or arrays of alternategeometry. Speakers 112 may comprise horn driver loudspeakers,electromechanical loudspeakers such as magnet-driver woofers and/orpiezoelectric speakers.

In order to carry out the functions of the DASS, the processor or othercomponents may manipulate or process sound signals sent to speakers 112.In particular, when speakers 112 comprise a line array or other type ofarray, sound signals may be sent to each speaker in the array. Thesesignals may be separately processed, or one or more signals may bejointly processed. The processing may be performed by analog or digitalcomponents and/or instructions, as described above. In particular, toexecute the functions of the DASS, the electronic device 100 may includeinstructions for adjusting a phase, amplitude, and/or delay of eachsound signal delivered to the speakers in the array. The phase,amplitude and/or delay may be controlled in such a manner as to producea desired coverage pattern, as described in greater detail below. Theadjustment of the phase, amplitude, and/or delay of speakers in thearray is discussed below with reference to FIG. 2.

The electronic device 100 may also include one or more cameras 113configured to capture and generate images. The one or more cameras 113may be digital cameras or charge-capture devices (CCDs) configured togenerate digital images of an environment of electronic device 100. Theone or more cameras may comprise optical cameras responsive to visuallight, infrared cameras, ultraviolet cameras, or other camerasappropriate to the application.

The electronic device 100 may also include one or more input devices 114configured to allow a user to interact with the components of theelectronic device. The one or more input devices 114 may include akeypad, a keyboard, and/or a cursor control device, such as a mouse, ora joystick. Also, the one or more input devices 114 may include a remotecontrol, touchscreen display, or other device operative to interact withthe electronic device 100, such as a device operative to act as aninterface between the electronic device and one or more users and/orother electronic devices.

The electronic device 100 may also include one or more sensors 116. Theone or more sensors 116 may include one or more proximity sensors,motion sensors, or cameras (such as found in a mobile device).Functionally, the one or more sensors 116 may include one or moresensors that detect or measure, motion, temperature, magnetic fields,gravity, humidity, moisture, vibration, pressure, electrical fields,sound, or other physical aspects associate with a potential user or anenvironment surrounding the user.

Turning now to FIG. 2, an example embodiment of the DASS is illustrated.

In this embodiment, the DASS may include an array 201 of speakers 201a-201 f communicatively coupled to processor 202. The processor maycomprise processor 102 in electronic device 100, and the speakers 201a-201 f in the array of speakers 201 may comprise speakers 112, forexample. The speakers 201 a-201 f may be coupled to processor 202 asshown. As shown in FIG. 2, speakers 201 a-201 f are coupled to processor202 by wired connections 203. In other examples, speakers 201 a-201 fmay communicate with processor 202 by wireless connection, Internet,local area network, Bluetooth, infrared, or others.

Speaker array 201 may be configured by the processor 202 to output soundwith a desired coverage pattern. The right side of FIG. 2 shows andexample coverage pattern 221 which may be generated by speaker array201. Coverage pattern 221 is shown in graphical form, wherein thevertical axis represents spatial distance or position 222 and thehorizontal axis represents sound pressure level (SPL) 223. The spatialposition 222 may be measured along an axis on which sound pressure levelis to be controlled; for example, when speaker array 201 comprises avertically oriented line array, position 222 may be representative of avertical distance (height) as measured at some predetermined distanceaway from the speaker array. Similarly, sound pressure level 223 mayalso be measured at the predetermined distance away from the speakerarray. As may be seen in plot 221, the coverage pattern of the speakerarray may vary with position. In this example, the coverage patternincludes a peak or lobe where sound intensity is the greatest, indicatedat 235. It will be appreciated that many different coverage patterns arepossible with a given speaker array. For example, the coverage patternmay include multiple peaks which may be of the same or differentintensity; the coverage pattern may include no peaks; the coveragepattern may be linearly increasing, decreasing, or constant; thecoverage pattern may follow a Gaussian, polynomial, power law, orexponential curve;

the coverage pattern may be defined piecewise, for example, includingcoverage patterns defined by piecewise linear or cubic splineinterpolation.

A desired coverage pattern may be obtained from speaker array 201 byappropriate means. The coverage pattern may be altered by altering thephase, delay, and/or amplitude of the sound signals reproduced by eachloudspeaker 201 a-201 f in the loudspeaker array 201. By appropriatelycontrolling the phase of each loudspeaker, the coverage pattern may bealtered in a desired manner. In one example, a desired coverage patternmay comprise a substantially uniform or “constant beamwidth” coveragepattern. In order to achieve this coverage pattern, a delay and/oramplitude of each speaker 201 a-201 f in the speaker array 201 may becontrolled. In this example, central speakers 201 c and 201 d may beselected to have substantially zero delay. Proximate speakers 201 b and201 e, which are immediately adjacent to and outside of central speakers201 c and 201 d, may then be selected to have a first delay. Speakers201 a and 201 f, immediately adjacent to and outside of speakers 201 cand 201 d, may then be selected to have a second delay, greater than thefirst delay. In other embodiments with a greater number of loudspeakersin the array, this pattern may continue in a similar fashion, withdelays progressively increasing in loudspeakers located further from thecenter of the array. The delays may be symmetric or asymmetric about thecentral loudspeaker(s).

The length of the first and second delays (and further delays, inembodiments with more loudspeakers) may be based on one or moreproperties of the speaker array 201, electronic device 100, and/or afrequency of reproduced sound. For example, when the goal is to improvespeaker response at a distance r_(o) from the central speaker(s) bygenerating constructive interference, a first delay di may be given by

$d_{1} = {\frac{1}{c_{s}}\left\lbrack {\sqrt{r_{0}^{2} + s_{1}^{2}} - r_{0}} \right\rbrack}$

where s₁ denotes a distance between the central speaker(s) and theneighboring speakers, and where c_(s) is the speed of sound. Thus, thesound generated by a second speaker or set of speakers may be delayedappropriately such that the sound from the first, central speaker(s),and the second, neighboring speakers may constructively interfere in adesired location or locations. A second delay, and further delays ifneeded, may be computed similarly, or by another appropriate method. Inother examples, the delay or phase of speakers in the array may becontrolled to produce destructive interference at desired locations,and/or mixed constructive and destructive interference at desiredlocations.

Controlling the coverage pattern produced by the speaker array 201 mayalso include controlling an amplitude of sound produced by each speaker201 a-201 f in the array. Continuing with the example of the constantbeamwidth coverage pattern, this may include selecting a first amplitudefor central speakers 201 c and 201 d; a second amplitude, lower than thefirst amplitude, for speakers 201 b and 201 e; and a third amplitude,lower than the second amplitude, for speakers 201 a and 201 f. This mayreduce the occurrence of off-axis interference effects and the presenceof “side lobes,” thereby increasing the uniformity of the coveragepattern and corresponding sound quality.

In another example, the phases and amplitudes of each speaker in thespeaker array may be controlled to produce the coverage pattern 221shown on the right side of FIG. 2. Methods similar to those discussedabove may be employed to produce coverage pattern 221, which may includeconstructive interference at location 235 in the coverage pattern,thereby producing a peak. The coverage pattern may also include regionsof substantial destructive interference, such as 237 a and 237 b, wherethe amplitude of the SPL signal is low or substantially zero. Thecoverage pattern may also include regions of mixed constructive anddestructive interference which may result in intermediate SPL values,such as those shown at 239 a and 239 b. To produce this coveragepattern, speaker 201 b may be selected to have substantially zero delay;speakers 201 a and 201 c may have a first delay; speaker 201 d may havea second delay longer than the first delay; and speakers 201 e and 201 fmay have progressively longer third and fourth delays, for example.Other appropriate beam steering techniques may be employed to achievethe desired coverage pattern.

The delay, phase, and/or amplitude adjustments assigned to the speakersin the speaker array 201 may be produced according to appropriatetechniques, which may include digital or analog instructions and/orcomponents. In one example, the adjustments may be generated by acascaded LC (inductor-capacitor) ladder network. In other examples, thephase, delay, and/or amplitude adjustments may be performed digitally bythe processor or other components, according to computer-readableinstructions stored in non-transitory memory.

In this manner, a desired coverage pattern may be obtained by assigningphase and amplitude values to each speaker in the array, therebycreating an array profile. However, at differing frequencies, thewavelengths of sound emitted by the speakers are different, thus adifferent coverage pattern may be produced by a given array profile atdifferent frequencies. For this reason, in order to obtain a stablecoverage pattern across different frequencies, different array profilesmay be generated at a plurality of different frequencies. Additionallyor alternatively, the array profile may vary smoothly as the frequencychanges. It is also conceivable that different coverage patterns may bedesired in different frequency bands, in which case different arrayprofiles may be selected accordingly.

Turning now to FIG. 3, an example high-level routine 300 is shown foradjusting speaker array coverage patterns based on facial detectiondata. Routine 300 may be performed by DAS system 100, in one example.

At 310, operational conditions of the DASS may be determined. Forexample, it may be determined whether a request has been made to updatethe coverage pattern of the system. A request may be received via inputdevice 114 from an operator, or the request may be automaticallygenerated after a predetermined time has elapsed since the most recentoccasion when the coverage pattern was adjusted. However, in someexamples, routine 300 may proceed automatically, without receiving arequest to update the coverage pattern. The operational condition ofcomponents of electronic device 100 may also be determined to ensurethere are no errors or faults. If an error or fault is detected or arequest is not made, routine 300 may optionally abort and/or alert auser that an error has occurred. Otherwise, processing proceeds to 320.

At 320, an image of a venue is taken with the camera. This could includetaking an image of an appropriate venue with camera 113. The venue isunderstood to be a space in which audience members are expected to be,and into which sound from the speakers is to be directed. Thus, thecamera and the speakers may be directed at the same venue, and thecamera may thereby be directed at an area into which sound will be aimedwith the speaker array according to the teachings of the presentdisclosure. The camera may be mounted near the speaker array, such asintegrally mounted in a housing containing the speaker array.Alternatively, the camera may be located at a distance from the speakerarray, but nevertheless be trained on the same venue. In one example,the speaker array may be located at a front end of a venue, while thecamera is located above, giving a “bird's eye” view of the audience. Inyet another example, two or more cameras may be mounted in differentlocations, and the images from each of the two or more cameras may beused in subsequent processing. After the image of the venue is acquired,processing proceeds to step 330.

At 330, facial detection is performed on the image. This may include,for example, passing the image from the camera to the processor, andperforming the facial detection with the processor. Facial detection maybe performed to generate facial detection data, to be used in subsequentprocessing. The facial detection in 330 may be performed by anappropriate method, such as template matching; vector-based approachessuch as the Eigenface method; neural network techniques; principalcomponent analysis; linear discriminant analysis; hidden Markov models;or another appropriate method. Additionally or alternatively,commercially available proprietary facial detection or detectionsoftware programs may be employed to generate facial detection data instep 330.

Facial detection may include identifying one or more faces in the image.The resultant facial detection data may include the number of faces inthe image, the location of each face in the image, and/or the size ofeach face in the image. In some examples, facial detection may associatea confidence value with each face. In other examples, facial recognitionalgorithms or other appropriate methods may be employed to generate thefacial detection data. After the facial detection is performed and thefacial detection data is generated, processing advances to 340.

At 340, the results of the facial detection performed at 330 areevaluated. If no faces are identified in the image, routine 300 proceedsto set a default target coverage pattern in 350. If one or more facesare identified, routine 300 proceeds to set a target coverage patternbased on the results of the facial detection at 360. Alternatively,routine 300 may proceed to 350 thereby using a default coverage pattern,if too few faces are detected. For example, routine 300 may proceed to350 if less than a threshold number of faces are detected, and proceedto 360 if a number of faces greater than or equal to the threshold isdetected. In determining whether faces are detected, or whether athreshold number of faces are detected, faces which are located too farfrom the speaker array, or at a periphery of the venue, may bedisregarded.

At 350, a default target coverage pattern is selected. The defaulttarget coverage pattern may be a desired coverage pattern, includinglinearly increasing, decreasing, constant, Gaussian, polynomial, powerlaw, exponential curve, or piecewise defined curve. In some examples,the target default coverage pattern may be based on a three-dimensionalmodel of the venue and respective DSP parameters. In other examples, thetarget default coverage pattern may not be based on a three-dimensionalmodel of the venue and/or respective DSP parameters. Alternatively,setting a default target coverage pattern at 350 may include disablingthe speakers until the facial detection processing detects one or more(or a threshold number of) faces, in order to conserve power. After thedefault target coverage pattern is selected, processing proceeds to 370.

At 360, a target coverage pattern is selected based on the results ofthe facial detection. This may include performing a cluster analysis onthe facial detection results, and selecting a coverage pattern based onthe size and location of the clusters. In another example, setting thetarget coverage pattern may include dividing the facial detection imageinto regions, computing the density of each region, and selecting acoverage pattern based on the density. Selecting the coverage patternbased on the facial detection may be performed without the use of athree-dimensional venue model or corresponding DSP parameters. Examplemethods for selecting a target coverage pattern based on the facialdetection data are elaborated below with reference to FIGS. 4-8. Afterroutine 300 has selected a target coverage pattern, processing proceedsto 370.

At 370, the target coverage pattern is applied to the speakers in thespeaker array. This may include adjusting a phase and an amplitude foreach speaker 201 a-201 f in the array of speakers 201 in order togenerate the desired coverage pattern, as described above with referenceto FIG. 2. After applying the desired coverage pattern to the speakerarray, processing proceeds to 380.

At 380, sound is reproduced via the speakers according to the desiredcoverage pattern. This may include operating the speakers in the speakerarray with the phase and amplitude profiles selected in 370, above. Thismay result in each speaker in the speaker array having a unique phaseand amplitude profile; however, in some examples, two or more speakersin the array may have identical profiles. Operating the speakers in thespeaker array according the profiles selected above produces sound withthe desired coverage pattern. Routine 300 then returns.

Routine 300 may be carried out before sound reproduction begins, e.g.,at the beginning of a performance. Additionally or alternatively,routine 300 may be carried out periodically during sound reproduction,or continually. In this manner, routine 300 may allow the DASS to updatethe coverage pattern periodically or in real time, as audiencedistribution changes throughout a performance, for example. Thus, ifaudience members enter the venue during sound reproduction, routine 300may automatically adjust the coverage area to send an appropriate SPL tothe new audience members in real time and without operator inputs.Conversely, if audience members exit the venue during soundreproduction, routine 300 may automatically adjust the coverage patternsuch that a lower SPL is being sent to the area previously occupied bythose audience members. This may also be performed in real time withoutoperator inputs. In this way, an optimal listening experience may beensured for all audience members, since sound will not be sent toregions of the venue where it is not needed. This may reduce powerconsumption of the DASS system, as well as reducing undesirablereverberation effects which may be caused by delivering sound to regionsof the venue with no audience members.

Turning now to FIGS. 4A-D, an example is depicted of selecting acoverage pattern based on the facial detection results. The methoddepicted in FIGS. 4A-D may be performed by routine 300 in step 360, forexample. FIG. 4A shows as example image 410 of a venue 405 which may becaptured by camera 113 at 320. Image 410 may include one or more faces407 of audience members populating the venue 405. Image 410 may besubjected to facial detection at 330, for example. In this example, theimage is taken from a camera which is mounted near or in the speakerarray, thus the perspective of the camera and the speaker array may besubstantially the same. However, the camera may be mounted in adifferent location. Further, in this example, the horizontal directionis assumed to be the direction along which the coverage pattern of thespeaker array may vary, although in other examples the speaker array mayvary its coverage pattern along a vertical direction, or otherdirection. Further, while this example depicts varying the coveragepattern along a single axis, further embodiments are contemplated inwhich the coverage pattern may be varied along two or more axes.

FIG. 4B shows an example result image 420 which results from the facialdetection. Individual faces 407 may be indicated by the facial detectionalgorithm. In this example, faces 407 are indicated by boxes 408bounding the faces, however it is not necessary that the faces beidentified visually as shown in FIG. 4B. For example, the facialdetection algorithm could return an annotated image, as shown in FIG.4B, or a list of pairs of locations, identified by for example x and ypixel values, where each pair of locations defines the boundary of aface. Alternatively, the facial detection algorithm could includeidentifying only the center of each face, or another appropriate point.Once the facial detection data is obtained, the DASS may identify acluster of faces in the result image.

FIG. 4C shows the result of identifying a cluster in the facialdetection image. In this case, the method may only identify a singlecluster 406. In FIG. 4C, all faces present in the venue are included incluster 406, but in another example, one or more faces may not beincluded in the cluster 406. The size and location of the cluster may bedetermined based on the locations of the individual faces, provided bythe facial detection algorithm. For example, in this case, the bounds ofthe cluster 406 may be obtained by searching for extreme high and lowvalues of the X and Y positions of each face in the cluster, where theboundaries of the cluster are then assumed to be equal to those extremevalues. Alternatively, the boundaries of the cluster may be set outsidethe extreme values by a predetermined amount. In other examples, thelocation of the cluster 406, or of more than one cluster, may bedetermined using other appropriate methods, such as cluster analysis,described in more detail below.

FIG. 4D shows an example of a coverage pattern based on the facialdetection and cluster identification above. FIG. 4D includes ahorizontal position axis 442, sound pressure level axis 443, andcoverage pattern 444. As discussed above, the coverage pattern 444 maybe measured at a predetermined distance from speaker array 221, forexample. In FIG. 4D, coverage pattern 444 is depicted as having a singlepeak 445, which is chosen to be located in the center of cluster 406.That is, the coverage pattern 444 may be chosen such that the coveragepattern 444 has the greatest SPL at the center of the cluster 406 ofaudience members present in the venue 405, based on the facial detectiondata. However, in other examples, the peak may be selected to be locatedat a different location inside the cluster, at an edge of the cluster,or outside the cluster. The coverage pattern may be selected to have twoor more peaks, in which all peaks may be located inside the cluster, forexample.

Continuing with this example, the magnitude 446 of the peak 445, that isthe SPL at the peak, may be selected based on the properties of thecluster. For example, the magnitude 446 of the peak may be increasedwith the physical size of the cluster, the density of the cluster(number of audience members divided by the area of the cluster), or thenumber of audience members present in the cluster. Additionally, a widthor dispersion 447 of the peak may be selected based on the properties ofthe cluster 406, for example, the width 447 may be increased as thewidth of the cluster increases or as population or density of thecluster increases. In one example, the coverage pattern may be selectedto provide an SPL greater than a first threshold to all areas inside thecluster. In another example, the coverage pattern may be selected toprovide an SPL less than a second threshold to all areas outside thecluster. The first and second thresholds may be the same threshold ordifferent thresholds.

Turning now to FIGS. 5A-D, another example is depicted of selecting acoverage pattern based on the facial detection results. The methoddepicted in FIGS. 5A- D may be performed by routine 300 in step 360, forexample. FIG. 5A shows an example image 510 of a venue 505 which may becaptured by camera 113 at 320. Image 510 may include one or more faces507 of audience members populating the venue 505. Image 510 may besubjected to facial detection at 330, for example. In this example, theimage is taken from a camera which is mounted near or in the speakerarray, thus the perspective of the camera and the speaker array may besubstantially the same. However, the camera may be mounted in adifferent location. Further, in this example, the horizontal directionis assumed to be the direction along which the coverage pattern of thespeaker array may vary, although in other examples the speaker array mayvary its coverage pattern along a vertical direction, or otherdirection. Further, while this example depicts varying the coveragepattern along a single axis, further embodiments are contemplated inwhich the coverage pattern may be varied along two or more axes.

FIG. 5B shows an example result image 520 which results from the facialdetection. Individual faces 507 may be indicated by the facial detectionalgorithm. In this example, faces 507 are indicated by boxes 508bounding the faces, however it is not necessary that the faces beidentified visually as shown in FIG. 5B. For example, the facialdetection algorithm could return an annotated image, as shown in FIG.5B, or a list of pairs locations, identified by for example X and Ypixel values, where each pair of locations defines the boundary of aface. Alternatively, the facial detection algorithm could includeidentifying only the center of each face, or another appropriate point.Once the facial detection data is obtained, DASS may identify one ormore clusters of faces in the result image.

FIG. 5C shows the result of identifying clusters in the facial detectionimage. In this case, the method identifies two clusters 506 a and 506 b,but the method may identify more or fewer clusters. In FIG. 5C, allfaces present in the venue are included in one of clusters 506 a and 506b, but in another example, one or more faces may not be included in acluster. The size and location of each cluster may be determined basedon the locations of the individual faces, provided by the facialdetection algorithm, by using an appropriate cluster analysis algorithm,such as those discussed below. FIG. 5C shows the result of one suchclustering algorithm.

FIG. 5D shows an example of a coverage pattern based on the facialdetection and cluster identification above. FIG. 5D includes ahorizontal position axis 542, a sound pressure level axis 543, and thecoverage pattern plot 544. As discussed above, the coverage pattern maybe measured at a predetermined distance from speaker array 221, forexample. In FIG. 5D, coverage pattern 544 is depicted as two peaks 545 aand 545 b which are chosen to be located in the center of clusters 506 aand 506 b, respectively. That is, the coverage pattern may be chosensuch that the coverage pattern has the greatest SPL at the center of oneor more clusters of audience members present in the venue, based on thefacial detection data. However, in other examples, the peaks may beselected to be located at a different location inside each cluster, atan edge of each cluster, or outside each cluster. In addition, themagnitude and width or dispersion of each peak may be adjusted accordingto the properties of each cluster, as described above with reference toFIG. 4D.

In this example, the magnitude (SPL) of each peak is selected based onthe number of audience members in each cluster. Thus, since cluster 506a has more audience members than cluster 506 b, the SPL at peak 545 a isshown as greater than the SPL at peak 545 b. Further, the width of eachpeak is selected based on a physical width of each cluster in thisexample. Thus the width of peak 545 a is wider than the width of peak545 b, since cluster 506 a is wider than cluster 506 b. The magnitudeand width of the peaks may be further selected based on other propertiesof the clusters, including population, density, size, area, shape, orposition. In other examples, the height and width of each peak may besubstantially the same, even though the properties of their respectiveclusters may vary. The peak magnitude and width may be independent ofcluster size, population, or density, and only the location of the peaksmay vary in some example. In embodiments where the coverage pattern doesnot include peaks, the coverage pattern may nevertheless be selectedbased on one or more of the cluster properties including population,density, size, area, shape, or position.

While FIG. 5C depicts clusters 506 a and 506 b as substantially squareor rectangular, this is not necessary. Clusters may comprise a suitableshape, such as circular, polygonal, or irregular shapes. The coveragepattern 544 may also be selected based on one or more properties of theregion lying outside of clusters 506 a and 506 b. For example, alocation of a trough 547 between peaks may be selected based on thelocation of a substantially empty region 509 in venue 505. The magnitudeand/or width of the trough 547 may similarly be determined by a size orshape of the empty region 509. The SPL in empty regions lying outside ofclusters of audience members may be selected to be less than the SPLdelivered to clusters of audience members.

Turning now to FIG. 6, an example high-level routine 600 is depicted.Routine 600 may include some or all of the steps depicted in FIGS. 4-5.Routine 600 may be performed as part of routine 300, at block 360.Routine 600 begins at step 610, where facial detection data is received.This may be the facial detection data obtained from step 330 in routine300, such as that depicted in FIGS. 4B and 5B.

At 620, the routine includes identifying clusters in the facialdetection data. This may be accomplished by an appropriate clusteridentification or analysis method. For example, identifying a clustermay proceed by searching for extreme high and low values of the X and Ypositions of each face in the cluster, where the boundaries of thecluster are then assumed to be equal to those extreme values.Alternatively, the boundaries of the cluster may be set outside theextreme values by a predetermined amount.

In other embodiments, the DASS may determine the clusters by adensity-based algorithm or other appropriate method. For example, step620 may include evaluating the position of each audience member in thefacial detection data to determine a number of other audience memberswithin a threshold distance. Audience members which have more than athreshold number of nearby other audience members may be designated“core” audience members. Any other (secondary) audience members whichare within the threshold distance of a core audience member may beconsidered a part of the same cluster as the core audience member; thisprocess may be repeated for other audience members which are within thethreshold distance from the (secondary) audience members, and so forth,to determine which groups of audience members belong to which clusters.In other examples, clusters of audience members may be determined usingappropriate cluster analysis methods, including hierarchical clustering,k-means clustering algorithm, expectation-maximization clusteringmethods, density-based clustering models such as DBSCAN, or other knownclustering techniques.

Once the audience members belonging to each cluster are determined, theboundaries of each cluster may be determined. This may include selectingan appropriate shape to surround the identified audience members, suchas rectangular clusters shown in FIGS. 4C and 5C. The cluster shape neednot be rectangular, but may also be circular, triangular, polygonal,elliptical, or irregular shapes, including 1-, 2-, or 3-dimensionalshapes, areas, or volumes. Alternatively, the area of the cluster may bedefined by the periphery formed by the outermost audience members ineach identified cluster. In still further examples, the DASS may includea fixed or predetermined number of clusters of a given size and/orshape. The DASS may be configured to arrange the clusters so as tomaximize the number of detected audience members which are included inthe clusters. Once clusters of audience members have been identified inthe facial detection data, routine 600 proceeds to 630.

At 630, routine 600 optionally includes discarding small clusters. Thismay include discarding clusters with a number of audience members belowa threshold, or discarding clusters with a physical dimension less thana threshold, such as a length, width, or area of the cluster. In anotherexample, at step 630, the routine may include discarding clusters whichhave an audience member density below a threshold. Additionally oralternatively, at 630, the routine may discard clusters which arelocated too close to a periphery of the venue or the camera's visualfield, e.g., within a threshold distance of the periphery. The routinemay also discard clusters which contain audience members which are toofar from the speaker array, such as greater than a threshold distanceaway from the speaker array. Once the selected clusters are discarded,the routine proceeds to 640.

At 640, routine 600 includes selecting a coverage pattern based on theclusters identified in the previous step. This may include selecting acoverage pattern using one or more of the methods described above withreference to FIGS. 4D and 5D. This may include selecting a location forone or more SPL peaks in the coverage pattern based on the locations ofthe clusters at 642, selecting a desired SPL (magnitude) at each peaklocation based on one or more properties of each cluster at 644, andselecting a width or dispersion of each peak based on one or moreproperties of each cluster at 646. This process is described in moredetail above. However, in other embodiments, block 640 may not includeblocks 642, 644, and 646. In some examples, the routine 600 may includeat 640 increasing the SPL of the coverage pattern in locations includingclusters of audience members and decreasing the SPL of the coveragepattern in locations not including clusters of audience members. Theroutine may include defining a desired SPL at a plurality of points andinterpolating between them to generate a coverage pattern, such as bygenerating a piecewise linear or cubic spline interpolation.Alternatively, the routine may define the desired SPL at a plurality ofpoints and determine a coverage pattern by regression analysis. In yetanother example, the method may include locally defined functions whichare stitched together at the endpoints or boundaries.

In some examples, the method may further select the desired SPL orcoverage pattern based on a distance between the speaker array or cameraand the detected audience members. For example, the method may includecomputing a distance of each face based on the detected size of eachface, determined previously in the facial detection routine.

The method may assign a greater distance to smaller faces and a lesserdistance to larger faces, for example. The routine may in some casescompute an average distance for all faces detected, or may compute anaverage distances for all faces within a cluster, for each cluster.Additionally or alternatively, the method may compute a distance oraverage distance of detected faces based on a computed distance betweennearest-neighbor faces.

For example, faces may be assumed to be closer to the camera whenindividual faces are farther apart in the facial detection data, andfaces may be assumed to be farther from the camera when individual facesare closer together in the facial detection data. This observation mayalso be used to compute an average distance of faces from the cameraand/or speaker array.

Having computed a distance of the faces, or an average distance of allfaces in the image or individual clusters, the method may includeselecting the coverage pattern based on the computed distance of thefaces from the camera and/or speaker array. For example, the coveragepattern may be selected to direct a higher SPL to audience members whoare farther from the source, and a lower SPL to audience members who arecloser to the source. In one example, this may comprise directing afirst, higher SPL to a first cluster, wherein the average distance ofthe audience members in the first cluster is a first, higher distance,and a second, lower SPL to a second cluster, wherein the averagedistance of the audience members in the second cluster is a second,lower distance from the speaker array. In other examples, the coveragepattern may be selected to direct a lower SPL to faces which are fartheraway and a higher SPL to faces which are closer. For example, thecoverage pattern may be selected such that a low or substantially zeroSPL is delivered to clusters in which the average distance of the facesfrom the source is greater than a threshold. This may prevent thedelivery of sound to regions where potential audience members are toofar away to hear. In this manner, a desired coverage pattern maycomposed and/or selected in step 640. Routine 600 then returns.

Turning now to FIGS. 7A-D, another example is depicted of selecting acoverage pattern based on the facial detection results. The methoddepicted in FIGS. 7A-D may be performed by routine 300 in step 360, forexample. FIG. 7A shows an example image 710 of a venue 705 which may becaptured by camera 113 at 320. Image 710 may include one or more faces707 of audience members populating the venue 705. Image 710 may besubjected to facial detection at 330, for example. In this example, theimage is taken from a camera which is mounted near or in the speakerarray, thus the perspective of the camera and the speaker array may besubstantially the same. However, the camera may be mounted in adifferent location. Further, in this example, the horizontal directionis assumed to be the direction along which the coverage pattern of thespeaker array may vary, although in other examples the speaker array mayvary its coverage pattern along a vertical direction, or otherdirection. Further, while this example depicts varying the coveragepattern along a single axis, further embodiments are contemplated inwhich the coverage pattern may be varied along two or more axes.

FIG. 7B shows an example result image 720 which results from the facialdetection. Individual faces 707 may be indicated by the facial detectionalgorithm. In this example, faces 707 are indicated by boxes 708bounding the faces, however it is not necessary that the faces beidentified visually as shown in FIG. 7B. For example, the facialdetection algorithm could return an annotated image, as shown in FIG.7B, or a list of pairs locations, identified by for example X and Ypixel values, where each pair of locations defines the boundary of aface. Alternatively, the facial detection algorithm could includeidentifying only the center of each face, or another appropriate point.Once the facial detection data is obtained, the DASS may identify thedensity of audience members in the resulting data.

FIG. 7C shows the result of identifying the density of audience membersin the facial detection image. In this example, the method proceeds bydividing the image of the venue 705 into regions, schematically depictedby broken lines 732 a-732 f which delimit the venue image into regions733 a-733 g. The number and/or locations of the regions may bepredetermined or, in some cases, may be dynamically determined based onthe number and/or distribution of the audience members. For example, thevenue may be broken up into a greater number of regions when there aremore audience members detected, to provide a finer resolution for thedesired coverage pattern. Further, while the depicted example showsbreaking the venue into regions along one axis only, in other examplesthe venue may be broken into regions along multiple axes, therebysegmenting the venue into 2- or 3-dimensional regions. The size, shape,number or location of the regions may be constant, or may vary as theaudience distribution changes, or according to other parameters. Forexample, the DASS may be configured to vary the number of regions basedon available processing capacity of the processor, employing a largernumber of regions when more processing power is available and a lowernumber of regions when less processing power is available.

The method may then count the number of audience members identified ineach region. In the image depicted in FIG. 7C, for example, region 733 aincludes 0 audience members, while region 733 f includes 6 audiencemembers. Additionally or alternatively, the method may compute anaudience density of each region by dividing the number of audiencemembers identified in each region by the respective area of the region.This may also include a weighting factor applied to each region, ortransforming the image to compensate for distortion due to the cameralens or the perspective of the venue. Once a density value for eachregion is obtained, a desired coverage pattern may be obtained.

FIG. 7D shows two example coverage patterns which may be obtained basedon the acquired audience density data. FIG. 7D includes a horizontaldistance axis 742, a sound pressure level axis 743, and two coveragepattern plots 746 and 748. A coverage pattern may be obtained byselecting a desired SPL in each region and interpolating between themusing, for example, piecewise linear interpolation, or cubic orquadratic spline interpolation. In another example, generating thecoverage pattern may include using regression analysis to fit a curve ofa desired form to the selected SPL levels in each region. The curve maybe one or more of linear, polynomial, Gaussian, exponential, power law,sinusoidal, or other appropriate curve. The SPL may be selected in eachregion to increase as the audience member density of the regionincreases and decrease as the audience member density decreases. Forexample, coverage pattern 748 may be selected such that the SPL issubstantially proportional to the population of each region, resultingin a coverage pattern with two local maxima 747 a and 747 b. Regionswithout audience members, or with a number of audience members below athreshold, may be supplied a SPL which is low or substantially zero. Inparticular, regions without audience members, or with a number ordensity of audience members below a threshold, may be supplied a lowerSPL than regions with audience members, or with a number or density ofaudience members above the threshold.

FIG. 7D also provides another example coverage pattern. In this example,coverage pattern 748 may be selected to provide an SPL above a thresholdSPL to all regions with at least a threshold number of audience members(in this example, the threshold number of audience members is 2), and toprovide an SPL below the threshold SPL to all regions with less than thethreshold number of audience members. The threshold SPL is depictedschematically at 750. In this example, the coverage pattern provides SPLgreater than the threshold to regions 733 c-733 f and provides SPL lessthan the threshold to regions 733 a, 733 b, and 733 g. In some examples,the coverage pattern may be selected to provide substantially identicalSPL to all regions with at least a threshold number of audience members,and substantially zero SPL to all other regions. However, in anotherexample, the SPL may vary between regions.

Turning now to FIG. 8, an example high-level routine 800 is depicted.

Routine 800 may include some or all of the steps depicted in FIG. 7.Routine 800 may be executed at block 360 in routine 300. Routine 800begins at 810, where the routine receives facial detection data. Thismay be the facial detection data obtained from step 330 in routine 300,such as that depicted in FIG. 7B.

At 820, the routine includes dividing the venue into regions. This maybe accomplished by means described above with reference to FIG. 7C. Insome examples, the routine may include dividing the venue along multipleaxes, to provide multi-dimensional density values in the followingprocessing steps. Once regions of the venue have been identified in thefacial detection data, routine 800 proceeds to 830.

At 830, routine 800 includes computing the density of each regionidentified in the previous step, as described above with reference toFIG. 7C. This may be accomplished by counting the audience members ineach region, giving the number of audience members per region.Alternatively, the method may include dividing each number by thecorresponding area of the region, thereby obtaining a number of audiencemembers per unit area in each region. This may include applying aweighting factor or transforming the image before or after computing thedensity. Once a density value is obtained for each region, the methodproceeds to 840.

At 840, routine 800 includes selecting a coverage pattern based on thedensities identified in the previous step. This may include selecting acoverage pattern using one or more of the methods described above withreference to FIG. 7D. This may include selecting a desired SPL for eachregion based on the identified density in each region. This may includeselecting a greater SPL in regions with greater density and a lower SPLin regions with lower density; selecting an SPL proportional to thedensity in each region; selecting a first, higher SPL in regions with adensity above a threshold and a second, lower SPL in regions with adensity below the threshold, and so forth. Once the routine has selecteda desired SPL in each region, processing proceeds to 850.

At 850, a target coverage pattern is composed based on the desired SPLin each region selected at 840. The routine may include interpolatingbetween desired SPL points to generate a coverage pattern, such as bygenerating a piecewise linear or cubic spline interpolation.Alternatively, the routine may determine a coverage pattern byregression analysis. In yet another example, the method may includelocally defined functions which are stitched together at the endpointsor boundaries, or another of the methods discussed above to generate acoverage pattern based on the desired SPL in each region. In thismanner, a desired coverage pattern may composed in step 850. Routine 800then returns.

Thus, in some embodiments, the above disclosure provides for a method,comprising adjusting a coverage pattern of a loudspeaker array based ona distribution of audience members. This method may include thedistribution of audience members being obtained from facial detectiondata; capturing an image of the audience members with a camera, andperforming facial detection on the image to obtain the facial detectiondata. Adjusting the coverage pattern may include increasing a firstsound intensity directed toward a first region and decreasing a secondsound intensity directed toward a second region. In this method, thefirst region may include one or more audience members, and the secondregion may not include audience members; or, the first region mayinclude a number of audience members greater than a threshold, and thesecond region may include a number of audience members less than athreshold.

In some examples, the first and second regions may comprise clusters ofaudience members, wherein the first and second sound intensities may beselected based on a number of audience members populating each of therespective first and second clusters; the first and second soundintensities may comprise peaks in the coverage pattern; a width of thepeaks may be selected based on a size of the respective first and secondclusters; the method may include decreasing a third sound intensitydirected toward a third region, the third sound intensity beingdecreased to less than the first and second sound intensities, whereinthe third region comprises a region outside the clusters of audiencemembers.

In other embodiments, adjusting the coverage pattern may includeadjusting a phase and an amplitude of respective loudspeakers in theloudspeaker array. Additionally or alternatively, adjusting the coveragepattern may be performed without a three-dimensional venue model orrespective digital signal processing parameters. The method may alsoinclude reproducing sound via the loudspeaker array based on theadjusted coverage pattern.

The above disclosure may also provide for a system, comprising aloudspeaker array including a plurality of loudspeakers directed towarda venue, a camera directed toward the venue, and a processor includingcomputer-readable instructions stored in non-transitory memory for:obtaining an image of the venue with the camera, processing the image toobtain facial detection data, and selecting a coverage pattern for theloudspeaker array based on the facial detection data. There may befurther instructions for determining a distribution of audience membersbased on the facial detection data, wherein selecting the coveragepattern is further based on the distribution. The distribution mayinclude an audience member density in each of one or more regions of thevenue. The coverage pattern may be further selected based on the densityof each of the one or more regions to direct greater sound intensity toregions with higher density and lower sound intensity to regions withlower density. The coverage pattern may be further selected based on thedensity of each of the one or more regions to direct a sound intensitygreater than a sound intensity threshold to regions with density higherthan a density threshold.

In another embodiment, the above disclosure may provide for a method,comprising receiving an image of a venue from a camera, performingfacial detection on the image to obtain facial detection data, analyzingthe facial detection data to obtain an audience member distribution,adjusting a coverage pattern of a loudspeaker array based on theaudience member distribution to direct a first, greater sound level to afirst region, and a second, lower sound level to a second region, andreproducing sound via the loudspeaker array. Analyzing the facialdetection data may include identifying one or more clusters of audiencemembers. The first and second regions may comprise first and secondclusters of the one or more clusters. The first region may comprise acluster of the one or more clusters, and the second region may beoutside of the one or more clusters. The coverage pattern may be basedon at least one property of the one or more clusters, the at least oneproperty including a number of audience members in the cluster, a sizeof the cluster, a density of the cluster, a location of the cluster, oran area of the cluster.

The description of embodiments has been presented for purposes ofillustration and description. Suitable modifications and variations tothe embodiments may be performed in light of the above description ormay be acquired from practicing the methods. For example, unlessotherwise noted, one or more of the described methods may be performedby a suitable device and/or combination of devices, such as theelectronic device 100 and/or loudspeaker array 201 and processor 202described with reference to FIGS. 1 and 2. The methods may be performedby executing stored instructions with one or more logic devices (e.g.,processors) in combination with one or more additional hardwareelements, such as storage devices, memory, hardware networkinterfaces/antennas, switches, actuators, clock circuits, etc. Thedescribed methods and associated actions may also be performed invarious orders in addition to the order described in this application,in parallel, and/or simultaneously. The described systems are exemplaryin nature, and may include additional elements and/or omit elements. Thesubject matter of the present disclosure includes all novel andnon-obvious combinations and sub-combinations of the various systems andconfigurations, and other features, functions, and/or propertiesdisclosed.

As used in this application, an element or step recited in the singularand proceeded with the word “a” or “an” should be understood as notexcluding plural of said elements or steps, unless such exclusion isstated. Furthermore, references to “one embodiment” or “one example” ofthe present disclosure are not intended to be interpreted as excludingthe existence of additional embodiments that also incorporate therecited features. The terms “first,” “second,” and “third,” etc. areused merely as labels, and are not intended to impose numericalrequirements or a particular positional order on their objects. Thefollowing claims particularly point out subject matter from the abovedisclosure that is regarded as novel and non-obvious.

1. A method, comprising adjusting a coverage pattern of a loudspeakerarray based on a distribution of audience members, the adjustingincluding increasing a first sound intensity directed toward a firstregion and decreasing a second sound intensity directed toward a secondregion, the first region including a two or three-dimensional space witha first perimeter of the space defined by a number of audience membersgreater than a threshold, the second region including a two orthree-dimensional space with a second perimeter of the space defined bya number of audience members less than a threshold.
 2. The method ofclaim 1, wherein the distribution of audience members is obtained fromfacial detection data, and wherein the coverage pattern is a spatialcoverage pattern.
 3. The method of claim 2, further comprising capturingan image of the audience members with a camera, and performing facialdetection on the image to obtain the facial detection data. 4.(canceled)
 5. The method of claim 1, wherein the first region includesone or more audience members, and wherein the second region does notinclude audience members.
 6. (canceled)
 7. The method of claim 1,wherein the first and second regions comprise clusters of audiencemembers, wherein the first and second sound intensities are selectedbased on a number of audience members populating each of the respectivefirst and second clusters, wherein the first and second soundintensities comprise peaks in the coverage pattern, wherein a width ofthe peaks is selected based on a size of the respective first and secondclusters, and further comprising decreasing a third sound intensitydirected toward a third region, the third sound intensity beingdecreased to less than the first and second sound intensities, whereinthe third region comprises a region outside the clusters of audiencemembers.
 8. The method of claim 1, wherein adjusting the coveragepattern further includes adjusting a delay and an amplitude ofrespective loudspeakers in the loudspeaker array in response to a changein the distribution of audience members; wherein adjusting the delay andamplitude includes increasing a first delay of a first speaker in thearray, decreasing a second delay of a second speaker in the array,decreasing a first amplitude of the first speaker, and increasing asecond amplitude of the second speaker; and wherein the change in thedistribution comprises an increase in a number of audience members in afirst direction, the first direction extending from the first speakertoward the second speaker.
 9. The method of claim 1, wherein adjustingthe coverage pattern is performed without a three-dimensional venuemodel or respective digital signal processing parameters.
 10. The methodof claim 1, further comprising reproducing sound via the loudspeakerarray based on the adjusted coverage pattern.
 11. A system, comprising aloudspeaker array including a plurality of loudspeakers directed towarda venue, a camera directed toward the venue, a processor includingcomputer-readable instructions stored in non-transitory memory for:obtaining an image of the venue with the camera, processing the image toobtain facial detection data, selecting a coverage pattern for theloudspeaker array based on the facial detection data, the coveragepattern including increasing a first sound intensity directed toward afirst region and decreasing a second sound intensity directed toward asecond region, the first region including a two or three-dimensionalspace with a first perimeter of the space defined by a number ofaudience members greater than a threshold, the second region including atwo or three-dimensional space with a second perimeter of the spacedefined by a number of audience members less than a threshold.
 12. Thesystem of claim 11, wherein the processor includes further instructionsfor determining a distribution of audience members based on the facialdetection data, and wherein selecting the coverage pattern is furtherbased on the distribution.
 13. The system of claim 12, wherein thedistribution includes an audience member density in each of one or moreregions of the venue.
 14. The system of claim 13, wherein the coveragepattern is further selected based on the density of each of the one ormore regions to direct greater sound intensity to regions with higherdensity and lower sound intensity to regions with lower density.
 15. Thesystem of claim 13, wherein the coverage pattern is further selectedbased on the density of each of the one or more regions to direct asound intensity greater than a sound intensity threshold to regions withdensity higher than a density threshold.
 16. A method, comprisingreceiving an image of a venue from a camera, performing facial detectionon the image to obtain facial detection data, analyzing the facialdetection data to obtain an audience member distribution, adjusting acoverage pattern of a loudspeaker array based on the audience memberdistribution to direct a first, greater sound level to a first region,and a second, lower sound level to a second region, and reproducingsound via the loudspeaker array with the adjusted coverage pattern, thefirst region including a two or three-dimensional space with a firstperimeter of the space defined by a number of audience members greaterthan a threshold, the second region including a two or three-dimensionalspace with a second perimeter of the space defined by a number ofaudience members less than a threshold.
 17. The method of claim 16,wherein analyzing the facial detection data includes identifying one ormore clusters of audience members.
 18. The method of claim 17, whereinthe first and second regions comprise first and second clusters of theone or more clusters, and wherein the first and second sound levels arebased on average distances of audience members in the first and secondregions, the average distances determined based on a size of faces inthe facial detection data.
 19. The method of claim 17, wherein the firstregion comprises a cluster of the one or more clusters, and the secondregion is outside of the one or more clusters.
 20. The method of claim17, wherein the coverage pattern is based on at least one property ofthe one or more clusters, the at least one property including a numberof audience members in the cluster, a linear dimension of the cluster, adensity of the cluster, a location of the cluster, a volume of thecluster, or an area of the cluster.